A backward stochastic differential equation (BSDE) is a stochastic differential equation with a terminal condition in which the solution is required to...

Deep backward stochastic differential equation method is a numerical method that combines deep learning with Backward stochastic differential equation...

Deep backward stochastic differential equation method is a numerical method that combines deep learning with Backward stochastic differential equation (BSDE)...

mechanics and information theory, the Fokker–Planck equation is a partial differential equation that describes the time evolution of the probability...

foundations. Deep backward stochastic differential equation method is a numerical method that combines deep learning with Backward stochastic differential equation...

extension of gradient descent, stochastic gradient descent, serves as the most basic algorithm used for training most deep networks today. Gradient descent...

runs. Asymptotic analysis Backward stochastic differential equation Calculus Copulas, including Gaussian Differential equations Expected value Ergodic theory...

probabilistic models, noise conditioned score networks, and stochastic differential equations. They are typically trained using variational inference. The...

players' state variables is governed by differential equations. The problem of finding an optimal strategy in a differential game is closely related to the optimal...

represented by stochastic state variables whose time evolution is given by the following system of stochastic differential equations: d F t = σ t ( F...

similarity of the two matrix Riccati differential equations, the first one running forward in time, the second one running backward in time. This similarity is...

Stephen Wolfram Stochastic block model Stochastic cellular automaton Stochastic diffusion search Stochastic grammar Stochastic matrix Stochastic universal sampling...

Caravelli–Traversa–Di Ventra equation. A continuous-time recurrent neural network (CTRNN) uses a system of ordinary differential equations to model the effects...

Bettencourt, Jesse; Duvenaud, David K. (2018). "Neural Ordinary Differential Equations" (PDF). In Bengio, S.; Wallach, H.; Larochelle, H.; Grauman, K.;...

core methodological paradigms of human experimental, cognitive, and differential psychology, but is also commonly analyzed in psychophysiology, cognitive...

in numerical methods for linear partial differential equations. His paper with Herman Goldstine in 1947 was the first to describe backward error analysis...

probabilistic models, noise conditioned score networks, and stochastic differential equations. Dijkstra's algorithm An algorithm for finding the shortest...

transient ocean surface wave Shallow water equations – Set of partial differential equations that describe the flow below a pressure surface in a fluid Tsunami –...

Monte Carlo methods for option pricing § Least Square Monte Carlo. When the Real Option can be modelled using a partial differential equation, then Finite...

driving force. One method for measuring the nucleation rate is through the induction time method. This process uses the stochastic nature of nucleation...

characterizes the device, and write it as a differential equation. The above table covers all meaningful ratios of differentials of I, q, Φm, and V. No device can...

solving equations of this type would yield infinitely large number of solutions, to which he then described a general method of solving such equations. Jayadeva's...

Shandong University "his pioneering work on the theory of backward stochastic differential equations, nonlinear Feynman-Kac formula, and the theory of nonlinear...

brain function. It considers the brain a dynamical system and uses differential equations to describe how neural activity evolves over time. In particular...

clumped isotopologues of methane, which should be enriched compared to the stochastic distribution at thermodynamic equilibrium because the reduced zero-point...

variable of interest, the posterior belief conditional on x first-order stochastically dominates the posterior conditional on y. Milgrom and others have used...